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Model: Codemaster67/olmo_chem_250k
Source: Original Platform

README.md

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+---
+base_model: Codemaster67/Olmo-7b-spe
+datasets:
+- Codemaster67/Causal_lm_chemistry_1M_rows
+language: en
+library_name: transformers
+license: apache-2.0
+tags:
+- chemistry
+- smiles
+- olmo
+- causal-lm
+- full-finetune
+- fsdp
+---
+
+# OLMo-7B Full Fine-Tune — Chemistry SMILES CPT
+
+## Model Description
+
+This model is a **full-parameter fine-tuned** version of
+[Codemaster67/Olmo-7b-spe](https://huggingface.co/Codemaster67/Olmo-7b-spe) trained on chemistry
+SMILES strings from the
+[Codemaster67/Causal_lm_chemistry_1M_rows](https://huggingface.co/datasets/Codemaster67/Causal_lm_chemistry_1M_rows) dataset.
+
+The base model's tokenizer was pre-extended with ~300 SPE (SMILES Pair
+Encoding) chemistry tokens plus `<|start_of_smiles|>` / `<|end_of_smiles|>`
+special tokens, and its embedding & LM-head layers were resized with
+mean-initialised vectors for the new tokens.
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| **Method** | Full Fine-Tune (all weights updated) |
+| **Parallelism** | FSDP (Fully Sharded Data Parallel) |
+| **Epochs** | 1 |
+| **Learning Rate** | 5e-06 |
+| **Batch Size (per device)** | 16 |
+| **Gradient Accumulation** | 1 |
+| **Max Sequence Length** | 512 |
+| **Warmup Ratio** | 0.1 |
+| **Weight Decay** | 0.01 |
+| **Scheduler** | Cosine |
+| **Precision** | bf16 |
+| **Augmentation** | OFF |
+| **Training Samples** | 250000 |
+| **Eval Samples** | 25000 |
+
+## Evaluation Results
+
+| Metric | Value |
+|---|---|
+| **Final Eval Loss** | 0.9727568626403809 |
+| **Final Eval Perplexity** | 2.645226943673604 |
+| **Training Loss** | 1.1177 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)
+
+smiles_input = "<|start_of_smiles|>CC(=O)Oc1ccccc1C(=O)O<|end_of_smiles|>"
+inputs = tokenizer(smiles_input, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=False))
+```
+
+## Intended Use
+
+Chemistry-domain language modelling, SMILES generation and completion,
+and downstream molecular property prediction via fine-tuning.
+
+## Limitations
+
+- Trained primarily on SMILES strings; natural-language instruction-following
+  ability may degrade compared to the base OLMo checkpoint.
+- Augmentation was disabled for this run.